In [ ]:
loaded_feeds.stops.plot(kind='scatter', x='stop_lon', y='stop_lat', s=0.1)
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f972da4ec10>
import pandas as pd
import pandana as pdna
import time
import urbanaccess as ua
from urbanaccess.config import settings
from urbanaccess.gtfsfeeds import feeds
from urbanaccess import gtfsfeeds
from urbanaccess.gtfs.gtfsfeeds_dataframe import gtfsfeeds_dfs
from urbanaccess.network import ua_network, load_network
%matplotlib inline
import warnings
import matplotlib.cbook
warnings.filterwarnings("ignore",category=matplotlib.cbook.mplDeprecation)
urbanaccess_net = ua.network.load_network(filename='final.h5')
Successfully read store: data/final.h5 with the following keys: ['/edges', '/nodes'] Successfully read store: data/final.h5 with the following keys: ['/edges', '/nodes']
import h5py
filename = "final.h5"
h5 = h5py.File(filename,'r')
list(h5.keys())
edges = h5['edges']
nodes = h5['nodes']
tbl1 = edges['table']
tbl1.keys()
# tbl2 = tbl1['index']
# tbl2.keys()
# tbl3 = nodes['_i_table']
# tbl4 = tbl1['index']
# tbl4.keys()
# h5.close()
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-23-f5b7c829145a> in <module> 11 12 tbl1 = edges['table'] ---> 13 tbl1.keys() 14 # tbl2 = tbl1['index'] 15 # tbl2.keys() AttributeError: 'Dataset' object has no attribute 'keys'
from google.colab import drive
drive.mount('/content/data')
Mounted at /content/data
validation = True
verbose = True
# bbox for Metro Vancouver
bbox = (-123.45, 48.99, -122.45, 49.49)
remove_stops_outsidebbox = True
append_definitions = True
file_path = 'C:\MDS\Capstone\DSCI_574_spat-temp-mod\DSCI_574_spat-temp-mod_students copy\lectures\Capstone_Data'
loaded_feeds = ua.gtfs.load.gtfsfeed_to_df(gtfsfeed_path=file_path,
validation=validation,
verbose=verbose,
bbox=bbox,
remove_stops_outsidebbox=remove_stops_outsidebbox,
append_definitions=append_definitions)
Checking GTFS text file header whitespace... Reading files using encoding: utf-8 set in configuration.
GTFS text file header whitespace check completed. Took 0.42 seconds
--------------------------------
Processing GTFS feed: Capstone_Data
The unique agency id: translink was generated using the name of the agency in the agency.txt file.
Unique agency id operation complete. Took 0.08 seconds
Unique GTFS feed id operation complete. Took 0.01 seconds
WARNING: Capstone_Data GTFS feed stops: 17 of 8,841 (0.19 percent of total) record(s) are outside the bounding box coordinates
Records:
stop_id stop_code stop_name \
7687 7911 57837.0 Eastbound Dewdney Trunk Rd @ 28300 Block (Flag)
7688 7912 57838.0 Southbound 284 St @ 11900 Block (Flag)
7689 7913 57839.0 Southbound 284 St @ 11600 Block (Flag)
7690 7914 57840.0 Southbound 280 St @ 11400 Block (Flag)
7691 7915 57841.0 Southbound 280 St @ 11300 Block (Flag)
7692 7917 57843.0 Southbound 280 St @ 11100 Block (Flag)
7693 7918 57844.0 Southbound 280 St @ 10800 Block (Flag)
7694 7919 57845.0 Southbound 280 St @ 10500 Block (Flag)
7696 7920 57846.0 Southbound 280 St @ 10300 Block (Flag)
7697 7921 57847.0 Southbound 280 St @ 10000 Block (Flag)
7698 7922 57848.0 Southbound 280 St @ 9800 Block (Flag)
7699 7925 57851.0 Southbound 280 St @ 9600 Block (Flag)
7700 7926 57852.0 Southbound 280 St @ Lougheed Hwy
7770 8026 57951.0 Mission City Station Unload Only
7771 8027 57952.0 Mission City Station Westbound
8759 9956 60979.0 Mission City Station @ Bay 1
8839 99957 NaN Mission City Station
stop_desc stop_lat stop_lon zone_id stop_url location_type \
7687 NaN 49.219860 -122.436985 BUS ZN NaN 0
7688 NaN 49.218721 -122.432439 BUS ZN NaN 0
7689 NaN 49.214854 -122.435339 BUS ZN NaN 0
7690 NaN 49.210632 -122.439311 BUS ZN NaN 0
7691 NaN 49.207429 -122.441828 BUS ZN NaN 0
7692 NaN 49.204870 -122.442718 BUS ZN NaN 0
7693 NaN 49.198655 -122.442350 BUS ZN NaN 0
7694 NaN 49.194229 -122.444046 BUS ZN NaN 0
7696 NaN 49.189951 -122.446608 BUS ZN NaN 0
7697 NaN 49.184256 -122.446562 BUS ZN NaN 0
7698 NaN 49.180190 -122.445359 BUS ZN NaN 0
7699 NaN 49.176661 -122.443681 BUS ZN NaN 0
7700 NaN 49.174293 -122.444274 BUS ZN NaN 0
7770 NaN 49.133594 -122.304860 WCE4Z NaN 0
7771 NaN 49.133785 -122.304872 WCE4Z NaN 0
8759 NaN 49.133882 -122.305153 BUS ZN NaN 0
8839 NaN 49.133694 -122.304898 WCE4Z NaN 1
parent_station unique_agency_id unique_feed_id
7687 NaN translink capstone_data_1
7688 NaN translink capstone_data_1
7689 NaN translink capstone_data_1
7690 NaN translink capstone_data_1
7691 NaN translink capstone_data_1
7692 NaN translink capstone_data_1
7693 NaN translink capstone_data_1
7694 NaN translink capstone_data_1
7696 NaN translink capstone_data_1
7697 NaN translink capstone_data_1
7698 NaN translink capstone_data_1
7699 NaN translink capstone_data_1
7700 NaN translink capstone_data_1
7770 99957.0 translink capstone_data_1
7771 99957.0 translink capstone_data_1
8759 NaN translink capstone_data_1
8839 NaN translink capstone_data_1
Removed identified stops that are outside of bounding box.
Capstone_Data GTFS feed stops: coordinates are in northwest hemisphere. Latitude = North (90); Longitude = West (-90).
Appended route type to stops
Appended route type to stop_times
--------------------------------
Added descriptive definitions to stops, routes, stop_times, and trips tables
Successfully converted ['departure_time'] to seconds past midnight and appended new columns to stop_times. Took 5.05 seconds
1 GTFS feed file(s) successfully read as dataframes:
Capstone_Data
Took 10.99 seconds
loaded_feeds.stops.head()
| stop_id | stop_code | stop_name | stop_desc | stop_lat | stop_lon | zone_id | stop_url | location_type | parent_station | unique_agency_id | unique_feed_id | route_type | location_type_desc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 50001.0 | Westbound Davie St @ Bidwell St | NaN | 49.286458 | -123.140424 | BUS ZN | NaN | 0 | NaN | translink | capstone_data_1 | 3.0 | stop |
| 1 | 10000 | 59326.0 | Northbound No. 5 Rd @ McNeely Dr | NaN | 49.179962 | -123.091490 | BUS ZN | NaN | 0 | NaN | translink | capstone_data_1 | 3.0 | stop |
| 2 | 10001 | 59324.0 | Northbound No. 5 Rd @ Woodhead Rd | NaN | 49.182670 | -123.091448 | BUS ZN | NaN | 0 | NaN | translink | capstone_data_1 | 3.0 | stop |
| 3 | 10002 | 59323.0 | Southbound No. 5 Rd @ Cambie Rd | NaN | 49.184252 | -123.091627 | BUS ZN | NaN | 0 | NaN | translink | capstone_data_1 | 3.0 | stop |
| 4 | 10003 | 59325.0 | Southbound No. 5 Rd @ Woodhead Rd | NaN | 49.182051 | -123.091659 | BUS ZN | NaN | 0 | NaN | translink | capstone_data_1 | 3.0 | stop |
loaded_feeds.stops.unique_agency_id.unique()
array(['translink'], dtype=object)
loaded_feeds.stops.plot(kind='scatter', x='stop_lon', y='stop_lat', s=0.1)
<matplotlib.axes._subplots.AxesSubplot at 0x7f972da4ec10>
loaded_feeds.routes.head()
| route_id | agency_id | route_short_name | route_long_name | route_desc | route_type | route_url | route_color | route_text_color | unique_agency_id | unique_feed_id | route_type_desc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 10232 | TL | 256 | Whitby Estate/Park Royal/Spuraway | NaN | 3 | NaN | NaN | NaN | translink | capstone_data_1 | Bus |
| 1 | 11201 | TL | 033 | 29th Avenue Station/UBC | NaN | 3 | NaN | NaN | NaN | translink | capstone_data_1 | Bus |
| 2 | 11202 | TL | 042 | Alma/Spanish Banks | NaN | 3 | NaN | NaN | NaN | translink | capstone_data_1 | Bus |
| 3 | 11692 | TL | 364 | Langley Centre/Scottsdale Exchange | NaN | 3 | NaN | NaN | NaN | translink | capstone_data_1 | Bus |
| 4 | 11693 | TL | 388 | 22nd St Station/Carvolth Exchange | NaN | 3 | NaN | NaN | NaN | translink | capstone_data_1 | Bus |
loaded_feeds.stop_times.head()
| trip_id | arrival_time | departure_time | stop_id | stop_sequence | stop_headsign | pickup_type | drop_off_type | shape_dist_traveled | unique_agency_id | unique_feed_id | route_type | pickup_type_desc | drop_off_type_desc | departure_time_sec | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 12495566 | 16:01:00 | 16:01:00 | 646 | 1 | NaN | 0 | 0 | NaN | translink | capstone_data_1 | 3 | Regularly Scheduled | Regularly Scheduled | 57660 |
| 1 | 12495566 | 16:13:54 | 16:13:54 | 1277 | 10 | NaN | 0 | 0 | 3.4113 | translink | capstone_data_1 | 3 | Regularly Scheduled | Regularly Scheduled | 58434 |
| 2 | 12495566 | 16:15:24 | 16:15:24 | 1279 | 11 | NaN | 0 | 0 | 3.8103 | translink | capstone_data_1 | 3 | Regularly Scheduled | Regularly Scheduled | 58524 |
| 3 | 12495566 | 16:17:30 | 16:17:30 | 56 | 12 | NaN | 0 | 0 | 4.3659 | translink | capstone_data_1 | 3 | Regularly Scheduled | Regularly Scheduled | 58650 |
| 4 | 12495566 | 16:18:25 | 16:18:25 | 12619 | 13 | NaN | 0 | 0 | 4.6100 | translink | capstone_data_1 | 3 | Regularly Scheduled | Regularly Scheduled | 58705 |
loaded_feeds.trips.head()
| route_id | service_id | trip_id | trip_headsign | trip_short_name | direction_id | block_id | shape_id | wheelchair_accessible | bikes_allowed | unique_agency_id | unique_feed_id | bikes_allowed_desc | wheelchair_accessible_desc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 6612 | 101 | 12495566 | 2 DOWNTOWN | NaN | 0 | 1991501 | 263376 | 0 | 0 | translink | capstone_data_1 | None | None |
| 1 | 6612 | 1 | 12495567 | 2 DOWNTOWN | NaN | 0 | 1991504 | 263375 | 0 | 0 | translink | capstone_data_1 | None | None |
| 2 | 6612 | 1 | 12495568 | 2 DOWNTOWN | NaN | 0 | 1991523 | 263375 | 0 | 0 | translink | capstone_data_1 | None | None |
| 3 | 6612 | 1 | 12495569 | 2 DOWNTOWN | NaN | 0 | 1991515 | 263375 | 0 | 0 | translink | capstone_data_1 | None | None |
| 4 | 6612 | 1 | 12495570 | 2 DOWNTOWN | NaN | 0 | 1991516 | 263375 | 0 | 0 | translink | capstone_data_1 | None | None |
loaded_feeds.calendar.head()
| service_id | monday | tuesday | wednesday | thursday | friday | saturday | sunday | start_date | end_date | unique_agency_id | unique_feed_id | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 20210412 | 20210620 | translink | capstone_data_1 |
| 1 | 101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20210412 | 20210620 | translink | capstone_data_1 |
| 2 | 1101 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20210412 | 20210620 | translink | capstone_data_1 |
| 3 | 1201 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20210412 | 20210620 | translink | capstone_data_1 |
| 4 | 152201 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 20210412 | 20210620 | translink | capstone_data_1 |
ua.gtfs.network.create_transit_net(gtfsfeeds_dfs=loaded_feeds,
day='monday',
timerange=['07:00:00', '10:00:00'],
calendar_dates_lookup=None)
Using calendar to extract service_ids to select trips. 1 service_ids were extracted from calendar 23,707 trip(s) 38.82 percent of 61,073 total trip records were found in calendar for GTFS feed(s): ['capstone data'] NOTE: If you expected more trips to have been extracted and your GTFS feed(s) have a calendar_dates file, consider utilizing the calendar_dates_lookup parameter in order to add additional trips based on information inside of calendar_dates. This should only be done if you know the corresponding GTFS feed is using calendar_dates instead of calendar to specify service_ids. When in doubt do not use the calendar_dates_lookup parameter. 23,707 of 61,073 total trips were extracted representing calendar day: monday. Took 0.10 seconds There are no departure time records missing from trips following the specified schedule. There are no records to interpolate. Difference between stop times has been successfully calculated. Took 6.00 seconds Stop times from 07:00:00 to 10:00:00 successfully selected 136,816 records out of 703,203 total records (19.46 percent of total). Took 0.18 seconds Starting transformation process for 5,355 total trips... stop time table transformation to Pandana format edge table completed. Took 12.19 seconds Time conversion completed: seconds converted to minutes. 8,429 of 8,824 records selected from stops. Took 0.02 seconds stop time table transformation to Pandana format node table completed. Took 0.01 seconds route type successfully joined to transit edges. Took 2.80 seconds route id successfully joined to transit edges. Took 0.14 seconds Successfully created transit network. Took 24.61 seconds
<urbanaccess.network.urbanaccess_network at 0x7f069ec7c690>
urbanaccess_net = ua.network.ua_network
urbanaccess_net.transit_edges.head()
| node_id_from | node_id_to | weight | unique_agency_id | unique_trip_id | sequence | id | route_type | unique_route_id | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55_translink | 56_translink | 1.283333 | translink | 12495573_translink | 1 | 12495573_translink_1 | 3 | 6612_translink | transit |
| 1 | 56_translink | 12619_translink | 1.016667 | translink | 12495573_translink | 2 | 12495573_translink_2 | 3 | 6612_translink | transit |
| 2 | 12619_translink | 58_translink | 0.666667 | translink | 12495573_translink | 3 | 12495573_translink_3 | 3 | 6612_translink | transit |
| 3 | 58_translink | 60_translink | 1.216667 | translink | 12495573_translink | 4 | 12495573_translink_4 | 3 | 6612_translink | transit |
| 4 | 60_translink | 61_translink | 0.750000 | translink | 12495573_translink | 5 | 12495573_translink_5 | 3 | 6612_translink | transit |
urbanaccess_net.transit_nodes.head()
| x | y | unique_agency_id | route_type | stop_id | stop_name | parent_station | stop_code | zone_id | location_type | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| node_id | |||||||||||
| 1_translink | -123.140424 | 49.286458 | translink | 3.0 | 1 | Westbound Davie St @ Bidwell St | NaN | 50001.0 | BUS ZN | 0 | transit |
| 10000_translink | -123.091490 | 49.179962 | translink | 3.0 | 10000 | Northbound No. 5 Rd @ McNeely Dr | NaN | 59326.0 | BUS ZN | 0 | transit |
| 10001_translink | -123.091448 | 49.182670 | translink | 3.0 | 10001 | Northbound No. 5 Rd @ Woodhead Rd | NaN | 59324.0 | BUS ZN | 0 | transit |
| 10002_translink | -123.091627 | 49.184252 | translink | 3.0 | 10002 | Southbound No. 5 Rd @ Cambie Rd | NaN | 59323.0 | BUS ZN | 0 | transit |
| 10003_translink | -123.091659 | 49.182051 | translink | 3.0 | 10003 | Southbound No. 5 Rd @ Woodhead Rd | NaN | 59325.0 | BUS ZN | 0 | transit |
urbanaccess_net.transit_nodes.plot(kind='scatter', x='x', y='y', s=0.1)
<matplotlib.axes._subplots.AxesSubplot at 0x7f9732ed2d10>
same bounding box
nodes, edges = ua.osm.load.ua_network_from_bbox(bbox=bbox,
remove_lcn=True)
Requesting network data within bounding box from Overpass API in 4 request(s)
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](48.99000000,-123.45000000,49.24108702,-122.94974726);>;);out;'}"
Downloaded 18,745.4KB from www.overpass-api.de in 3.43 seconds
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](49.24022154,-123.45000000,49.49108053,-122.94949183);>;);out;'}"
Downloaded 32,258.8KB from www.overpass-api.de in 5.55 seconds
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](49.23978874,-122.94974726,49.49108053,-122.45000000);>;);out;'}"
Downloaded 17,408.5KB from www.overpass-api.de in 17.93 seconds
Posting to http://www.overpass-api.de/api/interpreter with timeout=180, "{'data': '[out:json][timeout:180];(way["highway"]["highway"!~"motor|proposed|construction|abandoned|platform|raceway"]["foot"!~"no"]["pedestrians"!~"no"](48.99000000,-122.94999918,49.24108702,-122.45000000);>;);out;'}"
Downloaded 41,400.7KB from www.overpass-api.de in 9.98 seconds
Downloaded OSM network data within bounding box from Overpass API in 4 request(s) and 39.36 seconds
16,524 duplicate records removed. Took 8.90 seconds
Returning OSM data with 728,084 nodes and 156,266 ways...
Edge node pairs completed. Took 438.07 seconds
Returning processed graph with 227,724 nodes and 699,914 edges...
Completed OSM data download and Pandana node and edge table creation in 492.50 seconds
checking for low connectivity nodes...
248 out of 227,724 nodes (0.11 percent of total) were identified as having low connectivity and have been removed.
Completed OSM data download and graph node and edge table creation in 2,333.76 seconds
ua.osm.network.create_osm_net(osm_edges=edges,
osm_nodes=nodes,
travel_speed_mph=3)
Created OSM network with travel time impedance using a travel speed of 3 MPH. Took 0.03 seconds
<urbanaccess.network.urbanaccess_network at 0x7f069ec7c690>
urbanaccess_net.osm_nodes.head()
| x | y | id | net_type | |
|---|---|---|---|---|
| id | ||||
| 24657785 | -122.756605 | 49.162852 | 24657785 | walk |
| 24657870 | -122.845696 | 49.162805 | 24657870 | walk |
| 24658086 | -122.790872 | 49.162807 | 24658086 | walk |
| 24658104 | -122.794348 | 49.162810 | 24658104 | walk |
| 24658113 | -122.796734 | 49.162885 | 24658113 | walk |
urbanaccess_net.osm_edges.head()
| from | to | distance | name | ref | highway | service | bridge | tunnel | access | oneway | toll | lanes | maxspeed | hgv | hov | area | width | est_width | junction | weight | net_type | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7635734646 | 264765135 | 7635734646 | 264765135 | 51.877037 | Shell Road | NaN | tertiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | NaN | NaN | NaN | NaN | NaN | NaN | 0.644700 | walk |
| 264765135 | 7635734646 | 264765135 | 7635734646 | 51.877037 | Shell Road | NaN | tertiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | NaN | NaN | NaN | NaN | NaN | NaN | 0.644700 | walk |
| 5987979738 | 264765135 | 5987979738 | 17.263271 | Shell Road | NaN | tertiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | NaN | NaN | NaN | NaN | NaN | NaN | 0.214539 | walk | |
| 5987979738 | 264765135 | 5987979738 | 264765135 | 17.263271 | Shell Road | NaN | tertiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | NaN | NaN | NaN | NaN | NaN | NaN | 0.214539 | walk |
| 5987979740 | 5987979738 | 5987979740 | 30.734806 | Shell Road | NaN | tertiary | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 50 | NaN | NaN | NaN | NaN | NaN | NaN | 0.381955 | walk |
urbanaccess_net.osm_nodes.plot(kind='scatter', x='x', y='y', s=0.1)
<matplotlib.axes._subplots.AxesSubplot at 0x7f97142a3d50>
ua.network.integrate_network(urbanaccess_network=urbanaccess_net,
headways=False)
Loaded UrbanAccess network components comprised of:
Transit: 8,429 nodes and 131,461 edges;
OSM: 227,476 nodes and 699,472 edges
Connector edges between the OSM and transit network nodes successfully completed. Took 3.52 seconds
Edge and node tables formatted for Pandana with integer node ids: id_int, to_int, and from_int. Took 10.49 seconds
Network edge and node network integration completed successfully resulting in a total of 235,905 nodes and 847,791 edges:
Transit: 8,429 nodes 131,461 edges;
OSM: 227,476 nodes 699,472 edges; and
OSM/Transit connector: 16,858 edges.
<urbanaccess.network.urbanaccess_network at 0x7f069ec7c690>
urbanaccess_net.net_nodes.head()
| id | x | y | unique_agency_id | route_type | stop_id | stop_name | parent_station | stop_code | zone_id | location_type | net_type | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| id_int | ||||||||||||
| 1 | 1_translink | -123.140424 | 49.286458 | translink | 3.0 | 1 | Westbound Davie St @ Bidwell St | NaN | 50001.0 | BUS ZN | 0.0 | transit |
| 2 | 10000_translink | -123.091490 | 49.179962 | translink | 3.0 | 10000 | Northbound No. 5 Rd @ McNeely Dr | NaN | 59326.0 | BUS ZN | 0.0 | transit |
| 3 | 10001_translink | -123.091448 | 49.182670 | translink | 3.0 | 10001 | Northbound No. 5 Rd @ Woodhead Rd | NaN | 59324.0 | BUS ZN | 0.0 | transit |
| 4 | 10002_translink | -123.091627 | 49.184252 | translink | 3.0 | 10002 | Southbound No. 5 Rd @ Cambie Rd | NaN | 59323.0 | BUS ZN | 0.0 | transit |
| 5 | 10003_translink | -123.091659 | 49.182051 | translink | 3.0 | 10003 | Southbound No. 5 Rd @ Woodhead Rd | NaN | 59325.0 | BUS ZN | 0.0 | transit |
urbanaccess_net.net_edges.head()
| from | to | weight | unique_agency_id | unique_trip_id | sequence | edge_id | route_type | unique_route_id | net_type | distance | name | ref | highway | service | bridge | tunnel | access | oneway | toll | lanes | maxspeed | hgv | hov | area | width | est_width | junction | from_int | to_int | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55_translink | 56_translink | 1.283333 | translink | 12495573_translink | 1.0 | 12495573_translink_1 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5291 | 5388 |
| 1 | 56_translink | 12619_translink | 1.016667 | translink | 12495573_translink | 2.0 | 12495573_translink_2 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5388 | 1784 |
| 2 | 12619_translink | 58_translink | 0.666667 | translink | 12495573_translink | 3.0 | 12495573_translink_3 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1784 | 5592 |
| 3 | 58_translink | 60_translink | 1.216667 | translink | 12495573_translink | 4.0 | 12495573_translink_4 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5592 | 5777 |
| 4 | 60_translink | 61_translink | 0.750000 | translink | 12495573_translink | 5.0 | 12495573_translink_5 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5777 | 5873 |
urbanaccess_net.net_edges[urbanaccess_net.net_edges['net_type'] == 'transit'].head()
| from | to | weight | unique_agency_id | unique_trip_id | sequence | edge_id | route_type | unique_route_id | net_type | distance | name | ref | highway | service | bridge | tunnel | access | oneway | toll | lanes | maxspeed | hgv | hov | area | width | est_width | junction | from_int | to_int | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 55_translink | 56_translink | 1.283333 | translink | 12495573_translink | 1.0 | 12495573_translink_1 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5291 | 5388 |
| 1 | 56_translink | 12619_translink | 1.016667 | translink | 12495573_translink | 2.0 | 12495573_translink_2 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5388 | 1784 |
| 2 | 12619_translink | 58_translink | 0.666667 | translink | 12495573_translink | 3.0 | 12495573_translink_3 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 1784 | 5592 |
| 3 | 58_translink | 60_translink | 1.216667 | translink | 12495573_translink | 4.0 | 12495573_translink_4 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5592 | 5777 |
| 4 | 60_translink | 61_translink | 0.750000 | translink | 12495573_translink | 5.0 | 12495573_translink_5 | 3.0 | 6612_translink | transit | NaN | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | 5777 | 5873 |
Save the network
urbanaccess_net.net_edges.to_csv('net_edges.csv', index=False)
urbanaccess_net.net_nodes.to_csv('net_nodes.csv', index=False)
ua.network.save_network(urbanaccess_network=urbanaccess_net,
filename='final.h5', dir='/content/data/MyDrive/Capstone_Data',
overwrite_key = True)
New final.h5 hdf5 store created in dir: /content/data/MyDrive/Capstone_Data edges saved in /content/data/MyDrive/Capstone_Data/final.h5 hdf5 store. Using existing /content/data/MyDrive/Capstone_Data/final.h5 hdf5 store. nodes saved in /content/data/MyDrive/Capstone_Data/final.h5 hdf5 store.
urbanaccess_net = ua.network.load_network(dir='C:\MDS\Capstone\DSCI_574_spat-temp-mod\DSCI_574_spat-temp-mod_students copy\lectures\Capstone_Data', filename='final.h5')
Successfully read store: C:\MDS\Capstone\DSCI_574_spat-temp-mod\DSCI_574_spat-temp-mod_students copy\lectures\Capstone_Data/final.h5 with the following keys: ['/edges', '/nodes'] Successfully read store: C:\MDS\Capstone\DSCI_574_spat-temp-mod\DSCI_574_spat-temp-mod_students copy\lectures\Capstone_Data/final.h5 with the following keys: ['/edges', '/nodes']
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=bbox,
fig_height=30, margin=0.02,
edge_color='#999999', edge_linewidth=1, edge_alpha=1,
node_color='black', node_size=1.1, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 110.45 seconds
(<Figure size 4320x2160 with 1 Axes>, <matplotlib.axes._subplots.AxesSubplot at 0x7f19a250c810>)
edgecolor = ua.plot.col_colors(df=urbanaccess_net.net_edges, col='weight', cmap='gist_heat_r', num_bins=5)
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=bbox,
fig_height=30, margin=0.02,
edge_color=edgecolor, edge_linewidth=1, edge_alpha=0.7,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 109.57 seconds
(<Figure size 4320x2160 with 1 Axes>, <matplotlib.axes._subplots.AxesSubplot at 0x7f19482d3e50>)
# transit network
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges[urbanaccess_net.net_edges['net_type']=='transit'],
bbox=None,
fig_height=30, margin=0.02,
edge_color='#999999', edge_linewidth=1, edge_alpha=1,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 15.79 seconds
(<Figure size 4826.7x2160 with 1 Axes>, <matplotlib.axes._subplots.AxesSubplot at 0x7f190bd00090>)
# pedestrian network
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges[urbanaccess_net.net_edges['net_type']=='walk'],
bbox=None,
fig_height=30, margin=0.02,
edge_color='#999999', edge_linewidth=1, edge_alpha=1,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 87.39 seconds
(<Figure size 4172.42x2160 with 1 Axes>, <matplotlib.axes._subplots.AxesSubplot at 0x7f195fc17d10>)
calculate route stop level headways
ua.gtfs.headways.headways(gtfsfeeds_df=loaded_feeds,
headway_timerange=['07:00:00','10:00:00'])
Stop times from 07:00:00 to 10:00:00 successfully selected 136,816 records out of 703,203 total records (19.46 percent of total). Took 0.06 seconds Starting route stop headway calculation for 12,158 route stops... Route stop headway calculation complete. Took 32.69 seconds headway calculation complete. Took 34.51 seconds
<urbanaccess.gtfs.gtfsfeeds_dataframe.urbanaccess_gtfs_df at 0x7f069ed87250>
loaded_feeds.headways.head()
| count | mean | std | min | 25% | 50% | 75% | max | unique_stop_id | unique_route_id | node_id_route | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 111830 | 5.0 | 31.923333 | 3.155058 | 29.0 | 30.0 | 30.0 | 34.616667 | 36.0 | 10000_translink | 6829_translink | 10000_translink_6829_translink |
| 111854 | 5.0 | 31.923333 | 3.155058 | 29.0 | 30.0 | 30.0 | 34.616667 | 36.0 | 10000_translink | 6829_translink | 10000_translink_6829_translink |
| 111884 | 5.0 | 31.923333 | 3.155058 | 29.0 | 30.0 | 30.0 | 34.616667 | 36.0 | 10000_translink | 6829_translink | 10000_translink_6829_translink |
| 111914 | 5.0 | 31.923333 | 3.155058 | 29.0 | 30.0 | 30.0 | 34.616667 | 36.0 | 10000_translink | 6829_translink | 10000_translink_6829_translink |
| 111944 | 5.0 | 31.923333 | 3.155058 | 29.0 | 30.0 | 30.0 | 34.616667 | 36.0 | 10000_translink | 6829_translink | 10000_translink_6829_translink |
Add the route stop level average headways to integrated network
ua.network.integrate_network(urbanaccess_network=urbanaccess_net,
headways=True,
urbanaccess_gtfsfeeds_df=loaded_feeds,
headway_statistic='mean')
Loaded UrbanAccess network components comprised of:
Transit: 8,429 nodes and 131,461 edges;
OSM: 227,476 nodes and 699,472 edges
routes successfully joined to transit nodes. Took 0.23 seconds
Connector edges between the OSM and transit network nodes successfully completed. Took 4.37 seconds
mean route stop headway will be used for pedestrian to transit edge impedance.
Headway impedance calculation completed. Took 0.11 seconds
Edge and node tables formatted for Pandana with integer node ids: id_int, to_int, and from_int. Took 12.00 seconds
Network edge and node network integration completed successfully resulting in a total of 239,634 nodes and 979,907 edges:
Transit: 12,158 nodes 131,461 edges;
OSM: 227,476 nodes 699,472 edges; and
OSM/Transit connector: 148,974 edges.
<urbanaccess.network.urbanaccess_network at 0x7f069ec7c690>
edgecolor = ua.plot.col_colors(df=urbanaccess_net.net_edges, col='weight', cmap='gist_heat_r', num_bins=5)
ua.plot.plot_net(nodes=urbanaccess_net.net_nodes,
edges=urbanaccess_net.net_edges,
bbox=bbox,
fig_height=30, margin=0.02,
edge_color=edgecolor, edge_linewidth=1, edge_alpha=0.7,
node_color='black', node_size=0, node_alpha=1, node_edgecolor='none', node_zorder=3, nodes_only=False)
Figure created. Took 130.07 seconds
(<Figure size 4320x2160 with 1 Axes>, <matplotlib.axes._subplots.AxesSubplot at 0x7f1997b9bc10>)
Load block data
blocks = pd.read_csv('C:/MDS/Capstone/DSCI_574_spat-temp-mod/DSCI_574_spat-temp-mod_students copy/lectures/Capstone_Data/vancouver_db.csv')
print('Total number of blocks: {:,}'.format(len(blocks)))
blocks.head()
Total number of blocks: 15,197
| id | pop | lat | lon | |
|---|---|---|---|---|
| 0 | 59150004004 | 35 | 49.3739 | -123.2738 |
| 1 | 59150004005 | 50 | 49.3746 | -123.2757 |
| 2 | 59150004006 | 94 | 49.3738 | -123.2763 |
| 3 | 59150004011 | 0 | 49.3735 | -123.2725 |
| 4 | 59150004012 | 0 | 49.3725 | -123.2729 |
lng_max, lat_min, lng_min, lat_max = bbox
outside_bbox = blocks.loc[~(((lng_max < blocks["lon"]) & (blocks["lon"] < lng_min)) & ((lat_min < blocks["lat"]) & (blocks["lat"] < lat_max)))]
blocks_subset = blocks.drop(outside_bbox.index)
print('Total number of subset blocks: {:,}'.format(len(blocks_subset)))
Total number of subset blocks: 15,185
blocks_subset.plot(kind='scatter', x='lon', y='lat', s=0.1)
<AxesSubplot:xlabel='lon', ylabel='lat'>
Initialize Pandana network
s_time = time.time()
transit_ped_net = pdna.Network(urbanaccess_net.net_nodes["x"],
urbanaccess_net.net_nodes["y"],
urbanaccess_net.net_edges["from_int"],
urbanaccess_net.net_edges["to_int"],
urbanaccess_net.net_edges[["weight"]],
twoway=False)
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 32.45 seconds
blocks_subset['node_id'] = transit_ped_net.get_node_ids(blocks_subset['lon'], blocks_subset['lat'])
blocks_subset.head()
| id | pop | lat | lon | node_id | |
|---|---|---|---|---|---|
| 0 | 59150004004 | 35 | 49.3739 | -123.2738 | 80055 |
| 1 | 59150004005 | 50 | 49.3746 | -123.2757 | 174210 |
| 2 | 59150004006 | 94 | 49.3738 | -123.2763 | 50492 |
| 3 | 59150004011 | 0 | 49.3735 | -123.2725 | 93699 |
| 4 | 59150004012 | 0 | 49.3725 | -123.2729 | 70462 |
# calculate cumulative accessibility
transit_ped_net.set(blocks_subset.node_id, name='node_id')
s_time = time.time()
amens_90 = transit_ped_net.aggregate(90, type='sum', decay='linear', name='node_id')
amens_60 = transit_ped_net.aggregate(60, type='sum', decay='linear', name='node_id')
amens_30 = transit_ped_net.aggregate(30, type='sum', decay='linear', name='node_id')
print('Took {:,.2f} seconds'.format(time.time() - s_time))
# Pleaseeeeeeee...
s_time = time.time()
amens_60 = transit_ped_net.aggregate(60, type='sum', decay='linear', name='node_id')
print('Took {:,.2f} seconds'.format(time.time() - s_time))
s_time = time.time()
transit_ped_net.plot(amens_60,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
# bmap_kwargs={'epsg':'26943','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Took 3,676.66 seconds
Took 1.47 seconds
Amenities accessible within 30 minutes
s_time = time.time()
transit_ped_net.plot(amens_30,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
bmap_kwargs={'epsg':'26943','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Amenities accessible within 60 minutes
s_time = time.time()
transit_ped_net.plot(amens_60,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
bmap_kwargs={'epsg':'26943','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Amenities accessible within 90 minutes
s_time = time.time()
transit_ped_net.plot(amens_90,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
bmap_kwargs={'epsg':'26943','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
# configure search at a max distance of 10 km for up to the 10 nearest points of interest
types = pois['type'].unique()
distance = 25000
num_pois = 10
num_categories = len(types) + 1
# keyword arguments to pass for the matplotlib figure
# bbox_aspect_ratio = (bbox[2] - bbox[0]) / (bbox[3] - bbox[1])
fig_kwargs = {'facecolor':'w',
'figsize':(20, 20)}
# keyword arguments to pass for scatter plots
plot_kwargs = {'s':5,
'alpha':0.9,
'cmap':'viridis_r',
'edgecolor':'none'}
# network aggregation plots are the same as regular scatter plots, but without a reversed colormap
agg_plot_kwargs = plot_kwargs.copy()
agg_plot_kwargs['cmap'] = 'viridis'
# keyword arguments to pass for hex bin plots
hex_plot_kwargs = {'gridsize':60,
'alpha':0.9,
'cmap':'viridis_r',
'edgecolor':'none'}
# keyword arguments to pass to make the colorbar
cbar_kwargs = {}
# keyword arguments to pass to basemap
bmap_kwargs = {}
# color to make the background of the axis
bgcolor = 'k'
Load POIs and network data
start_time = time.time()
pois = pd.read_csv('C:/MDS/Capstone/DSCI_574_spat-temp-mod/DSCI_574_spat-temp-mod_students copy/lectures/Capstone_Data/vancouver_facilities_2.csv')
method = 'loaded from CSV'
print('{:,} POIs {} in {:,.2f} seconds'.format(len(pois), method, time.time()-start_time))
pois['type'].value_counts()
443 POIs loaded from CSV in 0.01 seconds
gallery 99 museum 92 library or archives 88 theatre/performance and concert hall 75 artist 48 heritage or historic site 28 miscellaneous 6 art or cultural centre 5 festival site 2 Name: type, dtype: int64
import numpy as np
pois = pois.replace('..', np.NaN)
pois = pois.dropna()
pois.count()
id 432 lat 432 lon 432 type 432 name 432 city 432 city_id 432 dtype: int64
pois['node_id'] = transit_ped_net.get_node_ids(pois['lon'], pois['lat'])
pois['node_id'].nunique()
398
pois.head()
| id | lat | lon | type | name | city | city_id | node_id | |
|---|---|---|---|---|---|---|---|---|
| 0 | 10 | 49.1763542 | -123.112783 | museum | 12 Service Battalion Museum | Richmond | 5915015 | 117661 |
| 1 | 15 | 49.261938 | -123.151123 | museum | 15th Field Artillery Regiment Museum And Archives | Vancouver | 5915022 | 24391 |
| 2 | 24 | 49.278786 | -123.098796 | museum | 221A Artist Run Centre | Vancouver | 5915022 | 177410 |
| 3 | 41 | 49.2210003 | -123.0091848 | artist | 7302754 Canada Inc | Burnaby | 5915025 | 56486 |
| 4 | 97 | 49.14709735 | -122.6467963 | heritage or historic site | Abc Heritage Preschool And Child Care | Langley | 5915001 | 156005 |
pois_gr = pois.groupby('node_id').size().reset_index()
pois_gr.loc[:,0].sum()
432
transit_ped_net.set(pois_gr.node_id, variable = pois_gr.loc[:,0], name='amens')
# precomputes the range queries (the reachable nodes within this maximum distance)
transit_ped_net.precompute(distance + 1)
# initialize the underlying points of interest engine
transit_ped_net.init_pois(num_categories=num_categories, max_dist=distance, max_pois=num_pois)
# initialize a category for all amenities with the locations specified by the lon and lat columns
transit_ped_net.set_pois(category='all', x_col=pois['lon'], y_col=pois['lat'])
Reading parameters from init_pois() Reading parameters from init_pois()
# searches for the n nearest amenities (of all types) to each node in the network
all_access = transit_ped_net.nearest_pois(distance=distance, category='all', num_pois=num_pois)
print('{:,} nodes'.format(len(all_access)))
all_access.head()
Facilities accessible within 120 minutes
s_time = time.time()
facilities_120 = transit_ped_net.aggregate(120, type='sum', decay='linear', name='amens')
print('Took {:,.2f} seconds'.format(time.time() - s_time))
s_time = time.time()
transit_ped_net.plot(facilities_120,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
bmap_kwargs={'epsg':'4141','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
s_time = time.time()
facilities_120 = transit_ped_net.aggregate(120, type='sum', decay='linear', name='amens')
print('Took {:,.2f} seconds'.format(time.time() - s_time))
s_time = time.time()
transit_ped_net.plot(facilities_120,
plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
# bmap_kwargs={'epsg':'4141','resolution':'h'},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
print('Took {:,.2f} seconds'.format(time.time() - s_time))
Plot accessibility from each node to any amenity
# distance to the nearest amenity of any type
n = 10
transit_ped_net.plot(all_access[n], bbox=bbox, plot_kwargs=plot_kwargs, fig_kwargs=fig_kwargs,
cbar_kwargs=cbar_kwargs)
# ax.set_axis_bgcolor(bgcolor)
# ax.set_title('Travel Time (min) to nearest amenity around Vancouver', fontsize=15)
# fig.savefig('images/accessibility-all-east-bay.png', dpi=200, bbox_inches='tight')
(<Figure size 1440x1440 with 2 Axes>, <AxesSubplot:>)
# distance to the nearest amenity of any type
n = 10
transit_ped_net.plot(all_access[n], plot_type='scatter',
fig_kwargs={'figsize':[20,20]},
plot_kwargs={'cmap':'gist_heat_r','s':4,'edgecolor':'none'})
(<Figure size 1440x1440 with 2 Axes>, <AxesSubplot:>)